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    <h1 id="a-naive-recursive-implementation-of-0-1-knapsack-problem">
      A naive recursive implementation of 0-1 Knapsack Problem
    </h1>
    <p>This overview is taken from:</p>
    <pre><code>https://en.wikipedia.org/wiki/Knapsack_problem</code></pre>
    <hr />
    <h2 id="overview">Overview</h2>
    <p>
      The knapsack problem is a problem in combinatorial optimization: Given a
      set of items, each with a weight and a value, determine the number of each
      item to include in a collection so that the total weight is less than or
      equal to a given limit and the total value is as large as possible. It
      derives its name from the problem faced by someone who is constrained by a
      fixed-size knapsack and must fill it with the most valuable items. The
      problem often arises in resource allocation where the decision makers have
      to choose from a set of non-divisible projects or tasks under a fixed
      budget or time constraint, respectively.
    </p>
    <p>
      The knapsack problem has been studied for more than a century, with early
      works dating as far back as 1897 The name “knapsack problem” dates back to
      the early works of mathematician Tobias Dantzig (1884–1956), and refers to
      the commonplace problem of packing the most valuable or useful items
      without overloading the luggage.
    </p>
    <hr />
    <h2 id="documentation">Documentation</h2>
    <p>
      This module uses docstrings to enable the use of Python’s in-built
      <code>help(...)</code> function. For instance, try
      <code>help(Vector)</code>, <code>help(unitBasisVector)</code>, and
      <code>help(CLASSNAME.METHODNAME)</code>.
    </p>
    <hr />
    <h2 id="usage">Usage</h2>
    <p>
      Import the module <code>knapsack.py</code> from the
      <strong>.</strong> directory into your project.
    </p>
    <hr />
    <h2 id="tests">Tests</h2>
    <p>
      <code>.</code> contains Python unit tests which can be run with
      <code>python3 -m unittest -v</code>.
    </p>
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